Urban building damage detection from very high resolution imagery using one-class SVM and spatial relations

In this paper, we propose a method for urban building damage detection from multitemporal high resolution images using spectral and spatial information combined. Given the spectral similarity between damaged and undamaged areas in the images, two spatial features are used in the damage detection, i.e. invariant moments and LISA (local indicator of spatial association) index. These two spatial features were computed for each image object, which is produced by image segmentation. The One-Class Support Vector Machine (OCSVM), a recently developed one-class classifier was used to classify the multitemporal data to obtain building damage information. The uses of spectral data alone and plus obtained spatial features for building damage detection were separately evaluated using bitemporal Quickbird images acquired in Dujiangyan area of China, which was heavily hit by the Wenchuan earthquake. The results show that the combined use of spectral and spatial features significantly improved the damage detection accuracy, compared to that of using spectral information alone.